Pandas Profiling Report

Dataset statistics

Number of variables	8
Number of observations	768
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	48.1 KiB
Average record size in memory	64.2 B

Variable types

Numeric	7
Categorical	1

Alerts

`SkinThickness` is highly overall correlated with `Insulin`	High correlation
`Insulin` is highly overall correlated with `SkinThickness`	High correlation
`BloodPressure` is highly overall correlated with `BMI`	High correlation
`BMI` is highly overall correlated with `BloodPressure`	High correlation
`BloodPressure` has 35 (4.6%) zeros	Zeros
`SkinThickness` has 227 (29.6%) zeros	Zeros
`Insulin` has 374 (48.7%) zeros	Zeros
`BMI` has 11 (1.4%) zeros	Zeros

Reproduction

Analysis started	2022-11-30 03:40:39.575666
Analysis finished	2022-11-30 03:40:54.385014
Duration	14.81 seconds
Software version	pandas-profiling vv3.5.0
Download configuration	config.json

Glucose
Real number (ℝ)

Distinct	136
Distinct (%)	17.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	121.11719

Minimum	0
Maximum	199
Zeros	5
Zeros (%)	0.7%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	0
5-th percentile	79
Q1	99
median	117
Q3	142
95-th percentile	180.65
Maximum	199
Range	199
Interquartile range (IQR)	43

Descriptive statistics

Standard deviation	31.805091
Coefficient of variation (CV)	0.26259767
Kurtosis	0.63709916
Mean	121.11719
Median Absolute Deviation (MAD)	20
Skewness	0.13293448
Sum	93018
Variance	1011.5638
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
100	17	2.2%
99	17	2.2%
150	15	2.0%
106	14	1.8%
129	14	1.8%
111	14	1.8%
125	13	1.7%
105	13	1.7%
112	13	1.7%
108	13	1.7%
Other values (126)	625	81.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	5	0.7%
44	1	0.1%
56	1	0.1%
57	2	0.3%
61	1	0.1%
62	1	0.1%
65	1	0.1%
67	1	0.1%
68	3	0.4%
71	4	0.5%

Value	Count	Frequency (%)
199	1	0.1%
198	1	0.1%
197	3	0.4%
196	3	0.4%
195	2	0.3%
194	3	0.4%
193	2	0.3%
191	1	0.1%
190	1	0.1%
189	3	0.4%

BloodPressure
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	47
Distinct (%)	6.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	69.076823

Minimum	0
Maximum	122
Zeros	35
Zeros (%)	4.6%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	0
5-th percentile	38.7
Q1	62
median	72
Q3	80
95-th percentile	90
Maximum	122
Range	122
Interquartile range (IQR)	18

Descriptive statistics

Standard deviation	19.367794
Coefficient of variation (CV)	0.28038049
Kurtosis	5.1502014
Mean	69.076823
Median Absolute Deviation (MAD)	8
Skewness	-1.8369976
Sum	53051
Variance	375.11143
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=47)

Value	Count	Frequency (%)
70	57	7.4%
74	52	6.8%
78	45	5.9%
68	45	5.9%
64	43	5.6%
72	43	5.6%
80	40	5.2%
76	39	5.1%
60	37	4.8%
0	35	4.6%
Other values (37)	332	43.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	35	4.6%
24	1	0.1%
30	2	0.3%
38	1	0.1%
40	1	0.1%
44	4	0.5%
46	2	0.3%
48	5	0.7%
50	14	1.8%
52	11	1.4%

Value	Count	Frequency (%)
122	1	0.1%
114	1	0.1%
110	3	0.4%
108	2	0.3%
106	3	0.4%
104	2	0.3%
102	1	0.1%
100	3	0.4%
98	3	0.4%
96	4	0.5%

SkinThickness
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	51
Distinct (%)	6.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	20.536458

Minimum	0
Maximum	99
Zeros	227
Zeros (%)	29.6%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	23
Q3	32
95-th percentile	44
Maximum	99
Range	99
Interquartile range (IQR)	32

Descriptive statistics

Standard deviation	15.952218
Coefficient of variation (CV)	0.77677549
Kurtosis	-0.52007187
Mean	20.536458
Median Absolute Deviation (MAD)	12
Skewness	0.1093725
Sum	15772
Variance	254.47325
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	227	29.6%
32	31	4.0%
30	27	3.5%
27	23	3.0%
23	22	2.9%
33	20	2.6%
28	20	2.6%
18	20	2.6%
31	19	2.5%
19	18	2.3%
Other values (41)	341	44.4%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	227	29.6%
7	2	0.3%
8	2	0.3%
10	5	0.7%
11	6	0.8%
12	7	0.9%
13	11	1.4%
14	6	0.8%
15	14	1.8%
16	6	0.8%

Value	Count	Frequency (%)
99	1	0.1%
63	1	0.1%
60	1	0.1%
56	1	0.1%
54	2	0.3%
52	2	0.3%
51	1	0.1%
50	3	0.4%
49	3	0.4%
48	4	0.5%

Insulin
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	186
Distinct (%)	24.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	79.799479

Minimum	0
Maximum	846
Zeros	374
Zeros (%)	48.7%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	30.5
Q3	127.25
95-th percentile	293
Maximum	846
Range	846
Interquartile range (IQR)	127.25

Descriptive statistics

Standard deviation	115.244
Coefficient of variation (CV)	1.4441699
Kurtosis	7.2142596
Mean	79.799479
Median Absolute Deviation (MAD)	30.5
Skewness	2.2722509
Sum	61286
Variance	13281.18
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	374	48.7%
105	11	1.4%
130	9	1.2%
140	9	1.2%
120	8	1.0%
94	7	0.9%
180	7	0.9%
100	7	0.9%
135	6	0.8%
115	6	0.8%
Other values (176)	324	42.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	374	48.7%
14	1	0.1%
15	1	0.1%
16	1	0.1%
18	2	0.3%
22	1	0.1%
23	2	0.3%
25	1	0.1%
29	1	0.1%
32	1	0.1%

Value	Count	Frequency (%)
846	1	0.1%
744	1	0.1%
680	1	0.1%
600	1	0.1%
579	1	0.1%
545	1	0.1%
543	1	0.1%
540	1	0.1%
510	1	0.1%
495	2	0.3%

BMI
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct	248
Distinct (%)	32.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	31.992578

Minimum	0
Maximum	67.1
Zeros	11
Zeros (%)	1.4%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	0
5-th percentile	21.8
Q1	27.3
median	32
Q3	36.6
95-th percentile	44.395
Maximum	67.1
Range	67.1
Interquartile range (IQR)	9.3

Descriptive statistics

Standard deviation	7.8841603
Coefficient of variation (CV)	0.24643717
Kurtosis	3.2904429
Mean	31.992578
Median Absolute Deviation (MAD)	4.6
Skewness	-0.42898159
Sum	24570.3
Variance	62.159984
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
32	13	1.7%
31.6	12	1.6%
31.2	12	1.6%
0	11	1.4%
32.4	10	1.3%
33.3	10	1.3%
30.1	9	1.2%
32.8	9	1.2%
32.9	9	1.2%
30.8	9	1.2%
Other values (238)	664	86.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	11	1.4%
18.2	3	0.4%
18.4	1	0.1%
19.1	1	0.1%
19.3	1	0.1%
19.4	1	0.1%
19.5	2	0.3%
19.6	3	0.4%
19.9	1	0.1%
20	1	0.1%

Value	Count	Frequency (%)
67.1	1	0.1%
59.4	1	0.1%
57.3	1	0.1%
55	1	0.1%
53.2	1	0.1%
52.9	1	0.1%
52.3	2	0.3%
50	1	0.1%
49.7	1	0.1%
49.6	1	0.1%

DiabetesPedigreeFunction
Real number (ℝ)

Distinct	517
Distinct (%)	67.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.4718763

Minimum	0.078
Maximum	2.42
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	0.078
5-th percentile	0.14035
Q1	0.24375
median	0.3725
Q3	0.62625
95-th percentile	1.13285
Maximum	2.42
Range	2.342
Interquartile range (IQR)	0.3825

Descriptive statistics

Standard deviation	0.3313286
Coefficient of variation (CV)	0.70215138
Kurtosis	5.5949535
Mean	0.4718763
Median Absolute Deviation (MAD)	0.1675
Skewness	1.9199111
Sum	362.401
Variance	0.10977864
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.258	6	0.8%
0.254	6	0.8%
0.268	5	0.7%
0.207	5	0.7%
0.261	5	0.7%
0.259	5	0.7%
0.238	5	0.7%
0.19	4	0.5%
0.263	4	0.5%
0.299	4	0.5%
Other values (507)	719	93.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.078	1	0.1%
0.084	1	0.1%
0.085	2	0.3%
0.088	2	0.3%
0.089	1	0.1%
0.092	1	0.1%
0.096	1	0.1%
0.1	1	0.1%
0.101	1	0.1%
0.102	1	0.1%

Value	Count	Frequency (%)
2.42	1	0.1%
2.329	1	0.1%
2.288	1	0.1%
2.137	1	0.1%
1.893	1	0.1%
1.781	1	0.1%
1.731	1	0.1%
1.699	1	0.1%
1.698	1	0.1%
1.6	1	0.1%

Age
Real number (ℝ)

Distinct	52
Distinct (%)	6.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	33.24349

Minimum	21
Maximum	81
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	6.1 KiB

Quantile statistics

Minimum	21
5-th percentile	21
Q1	24
median	29
Q3	41
95-th percentile	58
Maximum	81
Range	60
Interquartile range (IQR)	17

Descriptive statistics

Standard deviation	11.758182
Coefficient of variation (CV)	0.35369879
Kurtosis	0.64273721
Mean	33.24349
Median Absolute Deviation (MAD)	7
Skewness	1.1286322
Sum	25531
Variance	138.25485
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
22	72	9.4%
21	63	8.2%
25	48	6.2%
24	46	6.0%
23	38	4.9%
28	35	4.6%
26	33	4.3%
27	32	4.2%
29	29	3.8%
31	24	3.1%
Other values (42)	348	45.3%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
21	63	8.2%
22	72	9.4%
23	38	4.9%
24	46	6.0%
25	48	6.2%
26	33	4.3%
27	32	4.2%
28	35	4.6%
29	29	3.8%
30	21	2.7%

Value	Count	Frequency (%)
81	1	0.1%
72	1	0.1%
70	1	0.1%
69	2	0.3%
68	1	0.1%
67	3	0.4%
66	4	0.5%
65	3	0.4%
64	1	0.1%
63	4	0.5%

Outcome
Categorical

Distinct	2
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Memory size	6.1 KiB

0	500
1	268

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	768
Distinct characters	2
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	0
3rd row	1
4th row	0
5th row	1

Common Values

Value	Count	Frequency (%)
0	500	65.1%
1	268	34.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Value	Count	Frequency (%)
0	500	65.1%
1	268	34.9%

Most occurring characters

Value	Count	Frequency (%)
0	500	65.1%
1	268	34.9%

Most occurring categories

Value	Count	Frequency (%)
Decimal Number	768	100.0%

Most frequent character per category

Decimal Number

Value	Count	Frequency (%)
0	500	65.1%
1	268	34.9%

Most occurring scripts

Value	Count	Frequency (%)
Common	768	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
0	500	65.1%
1	268	34.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	768	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
0	500	65.1%
1	268	34.9%

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:

Variable_type-Variable_type : Method, Range
Categorical-Categorical : Cramer's V, [0,1]
Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
Numerical-Numerical : Spearman's ρ, [-1,1]

The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	148	50	35	0	33.6	0.627	50	1
1	85	66	29	0	26.6	0.351	31	0
2	183	64	0	0	23.3	0.672	52	1
3	150	66	23	94	28.1	0.167	21	0
4	150	40	35	168	43.1	2.288	33	1
5	150	74	0	0	25.6	0.201	30	0
6	150	50	32	88	31.0	0.248	26	1
7	150	0	0	0	35.3	0.134	29	0
8	150	70	45	543	30.5	0.158	35	1
9	150	96	0	0	0.0	0.232	54	1

	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
758	106	76	0	0	37.5	0.197	26	0
759	190	92	0	0	35.5	0.278	66	1
760	88	58	26	16	28.4	0.766	22	0
761	170	74	31	0	44.0	0.403	43	1
762	89	62	0	0	22.5	0.142	33	0
763	101	76	48	180	32.9	0.171	63	0
764	122	70	27	0	36.8	0.340	27	0
765	121	72	23	112	26.2	0.245	30	0
766	126	60	0	0	30.1	0.349	47	1
767	93	70	31	0	30.4	0.315	23	0

Overview

Variables

Common Values

Length

Common Values (Plot)

Most occurring characters

Most occurring categories

Most frequent character per category

Decimal Number

Most occurring scripts

Most frequent character per script

Common

Most occurring blocks

Most frequent character per block

ASCII

Interactions

Correlations

Auto

Spearman's ρ

Pearson's r

Kendall's τ

Phik (φk)

Missing values

Sample